Overview

Dataset statistics

Number of variables13
Number of observations18711
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory362.2 B

Variable types

NUM8
CAT5

Warnings

Agency Name has a high cardinality: 71 distinct values High cardinality
Total is highly correlated with City Funded Headcount and 1 other fieldsHigh correlation
City Funded Headcount is highly correlated with TotalHigh correlation
Other Funded Headcount is highly correlated with TotalHigh correlation
Personnel Type Name is highly correlated with Personnel Type CodeHigh correlation
Personnel Type Code is highly correlated with Personnel Type NameHigh correlation
Full-Time/Full-Time Equivalents/ FT + FTE Positions is highly correlated with Code for Full-Time & FTEsHigh correlation
Code for Full-Time & FTEs is highly correlated with Full-Time/Full-Time Equivalents/ FT + FTE PositionsHigh correlation
Code for Full-Time & FTEs is uniformly distributed Uniform
Full-Time/Full-Time Equivalents/ FT + FTE Positions is uniformly distributed Uniform
City Funded Headcount has 1438 (7.7%) zeros Zeros
IFA Funded Headcount has 16069 (85.9%) zeros Zeros
CD Funded Headcount has 15956 (85.3%) zeros Zeros
Other Funded Headcount has 12836 (68.6%) zeros Zeros
Total has 1378 (7.4%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:36:35.925393
Analysis finished2020-12-13 00:36:44.874093
Duration8.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Publication Date
Real number (ℝ≥0)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20179555.27
Minimum20160426
Maximum20200416
Zeros0
Zeros (%)0.0%
Memory size146.3 KiB
2020-12-12T19:36:44.950660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20160426
5-th percentile20160426
Q120170426
median20180426
Q320190425
95-th percentile20200416
Maximum20200416
Range39990
Interquartile range (IQR)19999

Descriptive statistics

Standard deviation12739.69431
Coefficient of variation (CV)0.0006313169017
Kurtosis-1.114251613
Mean20179555.27
Median Absolute Deviation (MAD)10000
Skewness0.03745318072
Sum3.775796587e+11
Variance162299811.1
MonotocityNot monotonic
2020-12-12T19:36:45.024223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
2019020711556.2%
 
2020041611556.2%
 
2017042611556.2%
 
2016061511556.2%
 
2020011611556.2%
 
2018020111556.2%
 
2017012411556.2%
 
2017060611556.2%
 
2018042611556.2%
 
2018061411556.2%
 
2019061911556.2%
 
2016042611556.2%
 
2019042511556.2%
 
201711219244.9%
 
201611179244.9%
 
201811089244.9%
 
201911229244.9%
 
ValueCountFrequency (%) 
2016042611556.2%
 
2016061511556.2%
 
201611179244.9%
 
2017012411556.2%
 
2017042611556.2%
 
2017060611556.2%
 
201711219244.9%
 
2018020111556.2%
 
2018042611556.2%
 
2018061411556.2%
 
ValueCountFrequency (%) 
2020041611556.2%
 
2020011611556.2%
 
201911229244.9%
 
2019061911556.2%
 
2019042511556.2%
 
2019020711556.2%
 
201811089244.9%
 
2018061411556.2%
 
2018042611556.2%
 
2018020111556.2%
 

Agency Number
Real number (ℝ≥0)

Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.2597403
Minimum2
Maximum945
Zeros0
Zeros (%)0.0%
Memory size146.3 KiB
2020-12-12T19:36:45.110297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q154
median132
Q3829
95-th percentile942
Maximum945
Range943
Interquartile range (IQR)775

Descriptive statistics

Standard deviation384.8362827
Coefficient of variation (CV)1.009380855
Kurtosis-1.701919314
Mean381.2597403
Median Absolute Deviation (MAD)121
Skewness0.432739683
Sum7133751
Variance148098.9645
MonotocityNot monotonic
2020-12-12T19:36:45.193869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
724862.6%
 
8274862.6%
 
564862.6%
 
574862.6%
 
424862.6%
 
404862.6%
 
9452431.3%
 
22431.3%
 
8162431.3%
 
9032431.3%
 
1032431.3%
 
712431.3%
 
9022431.3%
 
8062431.3%
 
1342431.3%
 
9442431.3%
 
1022431.3%
 
542431.3%
 
9012431.3%
 
1332431.3%
 
1012431.3%
 
172431.3%
 
692431.3%
 
212431.3%
 
8362431.3%
 
Other values (46)1117859.7%
 
ValueCountFrequency (%) 
22431.3%
 
32431.3%
 
42431.3%
 
82431.3%
 
102431.3%
 
112431.3%
 
122431.3%
 
132431.3%
 
142431.3%
 
152431.3%
 
ValueCountFrequency (%) 
9452431.3%
 
9442431.3%
 
9432431.3%
 
9422431.3%
 
9412431.3%
 
9062431.3%
 
9052431.3%
 
9042431.3%
 
9032431.3%
 
9022431.3%
 

Agency Name
Categorical

HIGH CARDINALITY

Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
Department of Education
 
486
Fire Department
 
486
Department of Correction
 
486
Department of Sanitation
 
486
Police Department
 
486
Other values (66)
16281 
ValueCountFrequency (%) 
Department of Education4862.6%
 
Fire Department4862.6%
 
Department of Correction4862.6%
 
Department of Sanitation4862.6%
 
Police Department4862.6%
 
City University4862.6%
 
Business Integrity Commission2431.3%
 
Department of Veterans' Services2431.3%
 
Independent Budget Office2431.3%
 
Conflicts of Interest Board2431.3%
 
Borough President - Bronx2431.3%
 
Department of Information Technology and Telecommunication2431.3%
 
Office of Prosecution and Special Narcotics2431.3%
 
Department of Consumer Affairs2431.3%
 
Department of Parks and Recreation2431.3%
 
Office of the Actuary2431.3%
 
Board of Correction2431.3%
 
Landmarks Preservation Commission2431.3%
 
Office of the Comptroller2431.3%
 
Administration for Children's Services2431.3%
 
Department of Design and Construction2431.3%
 
District Attorney - Brooklyn2431.3%
 
Board of Elections2431.3%
 
Department of Investigation2431.3%
 
Borough President - Manhattan2431.3%
 
Other values (46)1117859.7%
 
2020-12-12T19:36:45.287450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:36:45.373023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length58
Median length28
Mean length27.87012987
Min length9

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
507879.7%
 
e461708.9%
 
t456848.8%
 
n420398.1%
 
i379087.3%
 
o369367.1%
 
r335346.4%
 
a315906.1%
 
s204123.9%
 
m196833.8%
 
f148232.8%
 
c143372.7%
 
l121502.3%
 
d104492.0%
 
p102062.0%
 
D99631.9%
 
u85051.6%
 
C77761.5%
 
y68041.3%
 
v58321.1%
 
g58321.1%
 
P53461.0%
 
B53461.0%
 
A48600.9%
 
S41310.8%
 
Other values (27)303755.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41212879.0%
 
Uppercase Letter5321710.2%
 
Space Separator507879.7%
 
Dash Punctuation36450.7%
 
Other Punctuation7290.1%
 
Decimal Number4860.1%
 
Open Punctuation243< 0.1%
 
Close Punctuation243< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D996318.7%
 
C777614.6%
 
P534610.0%
 
B534610.0%
 
A48609.1%
 
S41317.8%
 
I24304.6%
 
E17013.2%
 
O17013.2%
 
T17013.2%
 
M14582.7%
 
H14582.7%
 
F12152.3%
 
R9721.8%
 
Q7291.4%
 
L7291.4%
 
U4860.9%
 
N4860.9%
 
Y4860.9%
 
V2430.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4617011.2%
 
t4568411.1%
 
n4203910.2%
 
i379089.2%
 
o369369.0%
 
r335348.1%
 
a315907.7%
 
s204125.0%
 
m196834.8%
 
f148233.6%
 
c143373.5%
 
l121502.9%
 
d104492.5%
 
p102062.5%
 
u85052.1%
 
y68041.7%
 
v58321.4%
 
g58321.4%
 
h38880.9%
 
b17010.4%
 
k14580.4%
 
x12150.3%
 
w7290.2%
 
q2430.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
50787100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3645100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'48666.7%
 
&24333.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(243100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
524350.0%
 
924350.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)243100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin46534589.2%
 
Common5613310.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e461709.9%
 
t456849.8%
 
n420399.0%
 
i379088.1%
 
o369367.9%
 
r335347.2%
 
a315906.8%
 
s204124.4%
 
m196834.2%
 
f148233.2%
 
c143373.1%
 
l121502.6%
 
d104492.2%
 
p102062.2%
 
D99632.1%
 
u85051.8%
 
C77761.7%
 
y68041.5%
 
v58321.3%
 
g58321.3%
 
P53461.1%
 
B53461.1%
 
A48601.0%
 
S41310.9%
 
h38880.8%
 
Other values (19)211414.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
5078790.5%
 
-36456.5%
 
'4860.9%
 
(2430.4%
 
52430.4%
 
92430.4%
 
)2430.4%
 
&2430.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII521478100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
507879.7%
 
e461708.9%
 
t456848.8%
 
n420398.1%
 
i379087.3%
 
o369367.1%
 
r335346.4%
 
a315906.1%
 
s204123.9%
 
m196833.8%
 
f148232.8%
 
c143372.7%
 
l121502.3%
 
d104492.0%
 
p102062.0%
 
D99631.9%
 
u85051.6%
 
C77761.5%
 
y68041.3%
 
v58321.1%
 
g58321.1%
 
P53461.0%
 
B53461.0%
 
A48600.9%
 
S41310.8%
 
Other values (27)303755.8%
 

Fiscal Year
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020
Minimum2016
Maximum2024
Zeros0
Zeros (%)0.0%
Memory size146.3 KiB
2020-12-12T19:36:45.444084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2023
Maximum2024
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.859294189
Coefficient of variation (CV)0.0009204426676
Kurtosis-0.5780991258
Mean2020
Median Absolute Deviation (MAD)1
Skewness0
Sum37796220
Variance3.45697488
MonotocityNot monotonic
2020-12-12T19:36:45.511142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2020392721.0%
 
2021323417.3%
 
2019323417.3%
 
2022231012.3%
 
2018231012.3%
 
202313867.4%
 
201713867.4%
 
20244622.5%
 
20164622.5%
 
ValueCountFrequency (%) 
20164622.5%
 
201713867.4%
 
2018231012.3%
 
2019323417.3%
 
2020392721.0%
 
2021323417.3%
 
2022231012.3%
 
202313867.4%
 
20244622.5%
 
ValueCountFrequency (%) 
20244622.5%
 
202313867.4%
 
2022231012.3%
 
2021323417.3%
 
2020392721.0%
 
2019323417.3%
 
2018231012.3%
 
201713867.4%
 
20164622.5%
 

Personnel Type Code
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
C
17253 
U
 
972
P
 
486
ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 
2020-12-12T19:36:45.593213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:36:45.643256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:45.693799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter18711100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin18711100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII18711100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 

Personnel Type Name
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
Civilian
17253 
Uniform
 
972
Pedagogical
 
486
ValueCountFrequency (%) 
Civilian1725392.2%
 
Uniform9725.2%
 
Pedagogical4862.6%
 
2020-12-12T19:36:45.766862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:36:45.815904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:45.874454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length8.025974026
Min length7

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i5321735.4%
 
a1822512.1%
 
n1822512.1%
 
l1773911.8%
 
C1725311.5%
 
v1725311.5%
 
o14581.0%
 
g9720.6%
 
U9720.6%
 
f9720.6%
 
r9720.6%
 
m9720.6%
 
P4860.3%
 
e4860.3%
 
d4860.3%
 
c4860.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter13146387.5%
 
Uppercase Letter1871112.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1725392.2%
 
U9725.2%
 
P4862.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i5321740.5%
 
a1822513.9%
 
n1822513.9%
 
l1773913.5%
 
v1725313.1%
 
o14581.1%
 
g9720.7%
 
f9720.7%
 
r9720.7%
 
m9720.7%
 
e4860.4%
 
d4860.4%
 
c4860.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin150174100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i5321735.4%
 
a1822512.1%
 
n1822512.1%
 
l1773911.8%
 
C1725311.5%
 
v1725311.5%
 
o14581.0%
 
g9720.6%
 
U9720.6%
 
f9720.6%
 
r9720.6%
 
m9720.6%
 
P4860.3%
 
e4860.3%
 
d4860.3%
 
c4860.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII150174100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i5321735.4%
 
a1822512.1%
 
n1822512.1%
 
l1773911.8%
 
C1725311.5%
 
v1725311.5%
 
o14581.0%
 
g9720.6%
 
U9720.6%
 
f9720.6%
 
r9720.6%
 
m9720.6%
 
P4860.3%
 
e4860.3%
 
d4860.3%
 
c4860.3%
 

Code for Full-Time & FTEs
Categorical

HIGH CORRELATION
UNIFORM

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
3
6237 
2
6237 
1
6237 
ValueCountFrequency (%) 
3623733.3%
 
2623733.3%
 
1623733.3%
 
2020-12-12T19:36:45.949519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:36:45.995059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:46.044101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1623733.3%
 
2623733.3%
 
3623733.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number18711100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1623733.3%
 
2623733.3%
 
3623733.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common18711100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1623733.3%
 
2623733.3%
 
3623733.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII18711100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1623733.3%
 
2623733.3%
 
3623733.3%
 

Full-Time/Full-Time Equivalents/ FT + FTE Positions
Categorical

HIGH CORRELATION
UNIFORM

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.3 KiB
Full-Time Plus Full-Time Equivalent
6237 
Full-Time
6237 
Full-Time Equivalent
6237 
ValueCountFrequency (%) 
Full-Time Plus Full-Time Equivalent623733.3%
 
Full-Time623733.3%
 
Full-Time Equivalent623733.3%
 
2020-12-12T19:36:46.113160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:36:46.157198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:46.212746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length20
Mean length21.33333333
Min length9

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l6860717.2%
 
u4365910.9%
 
i374229.4%
 
e374229.4%
 
F249486.2%
 
-249486.2%
 
T249486.2%
 
m249486.2%
 
249486.2%
 
E124743.1%
 
q124743.1%
 
v124743.1%
 
a124743.1%
 
n124743.1%
 
t124743.1%
 
P62371.6%
 
s62371.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter28066570.3%
 
Uppercase Letter6860717.2%
 
Dash Punctuation249486.2%
 
Space Separator249486.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F2494836.4%
 
T2494836.4%
 
E1247418.2%
 
P62379.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l6860724.4%
 
u4365915.6%
 
i3742213.3%
 
e3742213.3%
 
m249488.9%
 
q124744.4%
 
v124744.4%
 
a124744.4%
 
n124744.4%
 
t124744.4%
 
s62372.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-24948100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
24948100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34927287.5%
 
Common4989612.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l6860719.6%
 
u4365912.5%
 
i3742210.7%
 
e3742210.7%
 
F249487.1%
 
T249487.1%
 
m249487.1%
 
E124743.6%
 
q124743.6%
 
v124743.6%
 
a124743.6%
 
n124743.6%
 
t124743.6%
 
P62371.8%
 
s62371.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
-2494850.0%
 
2494850.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII399168100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l6860717.2%
 
u4365910.9%
 
i374229.4%
 
e374229.4%
 
F249486.2%
 
-249486.2%
 
T249486.2%
 
m249486.2%
 
249486.2%
 
E124743.1%
 
q124743.1%
 
v124743.1%
 
a124743.1%
 
n124743.1%
 
t124743.1%
 
P62371.6%
 
s62371.6%
 

City Funded Headcount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1922
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2414.015606
Minimum0
Maximum94726
Zeros1438
Zeros (%)7.7%
Memory size146.3 KiB
2020-12-12T19:36:46.287810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median85
Q3924.5
95-th percentile10930
Maximum94726
Range94726
Interquartile range (IQR)912.5

Descriptive statistics

Standard deviation9575.195404
Coefficient of variation (CV)3.966501037
Kurtosis68.15970428
Mean2414.015606
Median Absolute Deviation (MAD)85
Skewness7.81526958
Sum45168646
Variance91684367.03
MonotocityNot monotonic
2020-12-12T19:36:46.371883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
014387.7%
 
25543.0%
 
14912.6%
 
84712.5%
 
134212.3%
 
33912.1%
 
53581.9%
 
42661.4%
 
122581.4%
 
72211.2%
 
691841.0%
 
61831.0%
 
381710.9%
 
261570.8%
 
461510.8%
 
541510.8%
 
111420.8%
 
451380.7%
 
561340.7%
 
171240.7%
 
391210.6%
 
141150.6%
 
601080.6%
 
701070.6%
 
481060.6%
 
Other values (1897)1175062.8%
 
ValueCountFrequency (%) 
014387.7%
 
14912.6%
 
25543.0%
 
33912.1%
 
42661.4%
 
53581.9%
 
61831.0%
 
72211.2%
 
84712.5%
 
9670.4%
 
ValueCountFrequency (%) 
947265< 0.1%
 
947255< 0.1%
 
946763< 0.1%
 
946751< 0.1%
 
944932< 0.1%
 
944431< 0.1%
 
943273< 0.1%
 
943261< 0.1%
 
941335< 0.1%
 
941325< 0.1%
 

IFA Funded Headcount
Real number (ℝ≥0)

ZEROS

Distinct152
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.95521351
Minimum0
Maximum1734
Zeros16069
Zeros (%)85.9%
Memory size146.3 KiB
2020-12-12T19:36:46.457957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile144
Maximum1734
Range1734
Interquartile range (IQR)0

Descriptive statistics

Standard deviation208.3744128
Coefficient of variation (CV)4.740607454
Kurtosis34.05841047
Mean43.95521351
Median Absolute Deviation (MAD)0
Skewness5.747042713
Sum822446
Variance43419.89591
MonotocityNot monotonic
2020-12-12T19:36:46.543530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01606985.9%
 
12431.3%
 
521620.9%
 
81620.9%
 
581620.9%
 
31620.9%
 
142900.5%
 
144860.5%
 
7860.5%
 
6810.4%
 
14770.4%
 
9760.4%
 
768760.4%
 
767760.4%
 
139670.4%
 
11640.3%
 
140570.3%
 
635480.3%
 
641480.3%
 
288430.2%
 
16420.2%
 
292360.2%
 
293360.2%
 
289290.2%
 
25260.1%
 
Other values (127)6073.2%
 
ValueCountFrequency (%) 
01606985.9%
 
12431.3%
 
31620.9%
 
6810.4%
 
7860.5%
 
81620.9%
 
9760.4%
 
106< 0.1%
 
11640.3%
 
126< 0.1%
 
ValueCountFrequency (%) 
17341< 0.1%
 
17333< 0.1%
 
17321< 0.1%
 
17314< 0.1%
 
17304< 0.1%
 
17293< 0.1%
 
17282< 0.1%
 
17271< 0.1%
 
17181< 0.1%
 
17173< 0.1%
 

CD Funded Headcount
Real number (ℝ≥0)

ZEROS

Distinct111
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.42183742
Minimum0
Maximum1108
Zeros15956
Zeros (%)85.3%
Memory size146.3 KiB
2020-12-12T19:36:46.635109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile34
Maximum1108
Range1108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation99.91946395
Coefficient of variation (CV)7.44454435
Kurtosis101.5703259
Mean13.42183742
Median Absolute Deviation (MAD)0
Skewness10.00549017
Sum251136
Variance9983.899275
MonotocityNot monotonic
2020-12-12T19:36:46.720182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01595685.3%
 
14552.4%
 
24132.2%
 
341300.7%
 
101200.6%
 
221150.6%
 
36990.5%
 
5810.4%
 
3800.4%
 
58760.4%
 
7740.4%
 
16720.4%
 
9700.4%
 
8600.3%
 
11590.3%
 
117500.3%
 
12390.2%
 
49380.2%
 
6380.2%
 
153320.2%
 
24270.1%
 
1029270.1%
 
23270.1%
 
64240.1%
 
1075190.1%
 
Other values (86)5302.8%
 
ValueCountFrequency (%) 
01595685.3%
 
14552.4%
 
24132.2%
 
3800.4%
 
5810.4%
 
6380.2%
 
7740.4%
 
8600.3%
 
9700.4%
 
101200.6%
 
ValueCountFrequency (%) 
11081< 0.1%
 
11072< 0.1%
 
11052< 0.1%
 
11041< 0.1%
 
10989< 0.1%
 
10978< 0.1%
 
10941< 0.1%
 
10891< 0.1%
 
10845< 0.1%
 
10832< 0.1%
 

Other Funded Headcount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct445
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.8186628
Minimum0
Maximum32842
Zeros12836
Zeros (%)68.6%
Memory size146.3 KiB
2020-12-12T19:36:46.809259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile1221
Maximum32842
Range32842
Interquartile range (IQR)8

Descriptive statistics

Standard deviation2772.638408
Coefficient of variation (CV)6.917438395
Kurtosis101.4600756
Mean400.8186628
Median Absolute Deviation (MAD)0
Skewness9.921465641
Sum7499718
Variance7687523.74
MonotocityNot monotonic
2020-12-12T19:36:46.892330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01283668.6%
 
13181.7%
 
22841.5%
 
332301.2%
 
82201.2%
 
241961.0%
 
121680.9%
 
71640.9%
 
291640.9%
 
671630.9%
 
231550.8%
 
731400.7%
 
51370.7%
 
201340.7%
 
2891200.6%
 
3990.5%
 
21950.5%
 
108820.4%
 
22820.4%
 
71810.4%
 
69810.4%
 
264810.4%
 
3403730.4%
 
25570.3%
 
166530.3%
 
Other values (420)249813.4%
 
ValueCountFrequency (%) 
01283668.6%
 
13181.7%
 
22841.5%
 
3990.5%
 
4450.2%
 
51370.7%
 
6180.1%
 
71640.9%
 
82201.2%
 
9360.2%
 
ValueCountFrequency (%) 
32842180.1%
 
32578180.1%
 
30149140.1%
 
29885140.1%
 
28655120.1%
 
28534150.1%
 
28391120.1%
 
283088< 0.1%
 
28270150.1%
 
280448< 0.1%
 

Total
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2286
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2872.21132
Minimum0
Maximum127568
Zeros1378
Zeros (%)7.4%
Memory size146.3 KiB
2020-12-12T19:36:46.981908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median121
Q31250.5
95-th percentile10951
Maximum127568
Range127568
Interquartile range (IQR)1237.5

Descriptive statistics

Standard deviation12093.59867
Coefficient of variation (CV)4.210553239
Kurtosis80.13503031
Mean2872.21132
Median Absolute Deviation (MAD)121
Skewness8.574528608
Sum53741946
Variance146255128.9
MonotocityNot monotonic
2020-12-12T19:36:47.072986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
013787.4%
 
25182.8%
 
14752.5%
 
84482.4%
 
54082.2%
 
133772.0%
 
122751.5%
 
42641.4%
 
32331.2%
 
61791.0%
 
691770.9%
 
71750.9%
 
111630.9%
 
151620.9%
 
461570.8%
 
261560.8%
 
381430.8%
 
451380.7%
 
601330.7%
 
701330.7%
 
561260.7%
 
141260.7%
 
171230.7%
 
391220.7%
 
591140.6%
 
Other values (2261)1200864.2%
 
ValueCountFrequency (%) 
013787.4%
 
14752.5%
 
25182.8%
 
32331.2%
 
42641.4%
 
54082.2%
 
61791.0%
 
71750.9%
 
84482.4%
 
9130.1%
 
ValueCountFrequency (%) 
1275685< 0.1%
 
1275675< 0.1%
 
1273352< 0.1%
 
1267115< 0.1%
 
1267105< 0.1%
 
1266192< 0.1%
 
1264782< 0.1%
 
1264251< 0.1%
 
1259522< 0.1%
 
1259101< 0.1%
 

Interactions

2020-12-12T19:36:37.799506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:37.917607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.021197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.133293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.237383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.347477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.454570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.567167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.676761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.780350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.875932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:38.978020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.072101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.172187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.266268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.364852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.462437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.577536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.684628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.796725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:39.903817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.014412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.118501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.227095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.331685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.436275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.530856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.630942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.725023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.821606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:40.916688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.014773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.111856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.220950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.321036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.427128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.528715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.633805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.736394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.841484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:41.946074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.052666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.146747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.249335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.344417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.444503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.542087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.643674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.742259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.849852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:42.950438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.056530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.154614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.257703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.358790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.463380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.568470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.679566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.777150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.881239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:43.978823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:44.080411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:44.178996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:44.279582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:36:47.153555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:36:47.285169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:36:47.415281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:36:47.559405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T19:36:47.705530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T19:36:44.481756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:36:44.671419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Publication DateAgency NumberAgency NameFiscal YearPersonnel Type CodePersonnel Type NameCode for Full-Time & FTEsFull-Time/Full-Time Equivalents/ FT + FTE PositionsCity Funded HeadcountIFA Funded HeadcountCD Funded HeadcountOther Funded HeadcountTotal
020160426101Public Advocate2020CCivilian1Full-Time4000040
120160426781Department of Probation2018CCivilian2Full-Time Equivalent00000
22016061542City University2017CCivilian1Full-Time19070001907
3201604262Mayoralty2016CCivilian1Full-Time86013875591132
4201604262Mayoralty2016CCivilian2Full-Time Equivalent911112
5201604262Mayoralty2016CCivilian3Full-Time Plus Full-Time Equivalent86913976601144
6201604262Mayoralty2017CCivilian1Full-Time91413865231140
7201604262Mayoralty2017CCivilian2Full-Time Equivalent911011
8201604262Mayoralty2017CCivilian3Full-Time Plus Full-Time Equivalent92313966231151
9201604262Mayoralty2018CCivilian1Full-Time91013838211107

Last rows

Publication DateAgency NumberAgency NameFiscal YearPersonnel Type CodePersonnel Type NameCode for Full-Time & FTEsFull-Time/Full-Time Equivalents/ FT + FTE PositionsCity Funded HeadcountIFA Funded HeadcountCD Funded HeadcountOther Funded HeadcountTotal
1870120200416945Public Administrator - Staten Island2021CCivilian3Full-Time Plus Full-Time Equivalent50005
1870220200416945Public Administrator - Staten Island2022CCivilian1Full-Time50005
1870320200416945Public Administrator - Staten Island2022CCivilian2Full-Time Equivalent00000
1870420200416945Public Administrator - Staten Island2022CCivilian3Full-Time Plus Full-Time Equivalent50005
1870520200416945Public Administrator - Staten Island2023CCivilian1Full-Time50005
1870620200416945Public Administrator - Staten Island2023CCivilian2Full-Time Equivalent00000
1870720200416945Public Administrator - Staten Island2023CCivilian3Full-Time Plus Full-Time Equivalent50005
1870820200416945Public Administrator - Staten Island2024CCivilian1Full-Time50005
1870920200416945Public Administrator - Staten Island2024CCivilian2Full-Time Equivalent00000
1871020200416945Public Administrator - Staten Island2024CCivilian3Full-Time Plus Full-Time Equivalent50005